In the course of this thesis a hybrid mathematical model for the simulation and prediction of influenza epidemics is going to be established.

The classic methods applied for modelling such epidemics used to be ODE-Systems but unfortunately this systems are limited in some respect.

They become particularly complicated and complex beyond limit when observing heterogeneous populations and spatial components. Thus the potential of alternative approaches -- namely cellular automata and agent based systems -- is analysed in the beginning of this work.

Analysis of these methods was split into two major parts. The first one being the theoretical one in which the methods were compared in order to locate their respective strengths and weaknesses. The second part being the practical analysis including behaviour of the implementations, for this a simple SIR epidemic was modelled with each approach.

Backed by the findings of the analysis of the methods, the final hybrid model was set up. Since accurate parametrisation of models requires reliable and authentic data for validation purposes, and due to the fact that a further development of the model strongly depends on the quality of this data, this issue is also covered in this thesis. Finally the experiments with the newly created model are analysed.

The outcome of the experiments backs the basic motivation to use a hybrid approach and encourages further investigation of it. Thus an outline for future possibilities of and necessities for such modelling approaches is sketched.